In recent years, the field of artificial intelligence and machine learning has experienced a resurgence due to advances in performance of computer hardware, sizes of training sets, theoretical understanding of artificial intelligence, and other advances. This resurgence has enabled many advances in other technical fields, including recognition or other prediction systems. One application of automated recognition systems include detection of brand logos in images and videos shared on social media or other platforms, which may be used to develop insights into the brands, including interest of users with respect to the brands and their products/services, the types of users that like (or dislike) particular brands, or other information.
In typical artificial-intelligence-based recognition systems, a recognition model is trained to recognize logos of different appearances by collecting and hand-annotating large sets of images in which the logos appear in many different settings (e.g., photographs, ads, etc.) from many different views (e.g., left-perspective billboard, front-perspective on a T-shirt, etc.) in order to encompass a wide enough variety of appearance to obtain acceptable performance. Even with computer-assisted searches, the collection of the large sets of images (in which a given logo appears) for use in training a recognition model to recognize the given logo can be resource intensive and time consuming. Moreover, a collection of large sets of images (in which a given logo appears) may be difficult or impossible to obtain via computer-assisted searches, especially when the given logo is new and has not (or has only recently) been released to the public. These and other drawbacks exist.